Splitting neurons into fragments for sampling, aSPg example

1. Load your neuron in CATMAID and check the root of the neuron is at the soma and you have tagged all the nodes you want to prune from.

#Load all the necessary libraries
library(catmaid)
library(elmr)

skid <- 534333
n <- read.neuron.catmaid(skid) #aSPg neuron

Cut neuron at specified tagged nodes, in this case “AJES_cut_x”

#Get the nodes for the tags with "AJES_cut" in them
cut_nodes <- n$tags[grepl("AJES_cut", names(n$tags))]
#Reorder to AJES_cut_1, AJES_cut_2, AJES_cut_3, AJES_cut_4
cut_nodes <- cut_nodes[sort(names(cut_nodes))]
#Get X,Y,Z coords of cut_nodes to plot in 3d
n$d$X[n$d$PointNo %in% cut_nodes]

[1] 631533 632757 631631 629468

n$d$Y[n$d$PointNo %in% cut_nodes]

[1] 150459 149966 162402 158085

n$d$Z[n$d$PointNo %in% cut_nodes]

[1] 80000 81520 89720 91400

nview3d("frontal")
plot3d(n, soma = T, col = "black", WithConnectors = T)
col <- rainbow(length(cut_nodes))
sapply(1:4, function(x) points3d(n$d$X[n$d$PointNo %in% cut_nodes[x]], 
                                 n$d$Y[n$d$PointNo %in% cut_nodes[x]],
                                 n$d$Z[n$d$PointNo %in% cut_nodes[x]],
                                 size = 10,
                                 col = col[x],
                                 pch = 16)) #pch = 16 plots circles, use ?points to find other shapes plotted with the pch argument.

[1] 76 77 78 79

legend3d("topright", legend = names(cut_nodes), fill = col)
rglwidget()

#Lets start by cutting the neuron distal to AJES_cut_1.
dist <- distal_to(n, node.pointno = cut_nodes[[1]]) #Return indices of points in a neuron distal to a given node
#Subset our neuron by dist indices
neuron.distal.points <- n$d[dist,]

Plot points distal to cut point 1.

clear3d()
legend3d()

Error in as.graphicsAnnot(legend) : 
  argument "legend" is missing, with no default

nview3d("frontal")
plot3d(n, col = "gray23", soma = T)
points3d(neuron.distal.points[,c('X','Y','Z')], col = "deepskyblue")
rglwidget()

#That's great, but we are only interested in the axons on the left hemisphere. Not the ones on the right too.
#Lets remove everything distal to AJES_cut_2 from everything distal to AJES_cut1
#Lets start by cutting the neuron distal to AJES_cut_1.
dist1 <- distal_to(n, node.pointno = cut_nodes[[1]])
dist2 <- distal_to(n, node.pointno = cut_nodes[[2]])
#Subset our neuron by dist indices
neuron.distal.points <- n$d[setdiff(dist1, dist2),]

clear3d()
nview3d("frontal")
plot3d(n, col = "gray23", soma = T)
points3d(neuron.distal.points[,c('X','Y','Z')], col = "deepskyblue")
rglwidget()

# Or there's the option of including points distal to two cuts...
dist3 <- distal_to(n, node.pointno = cut_nodes[[3]])
dist4 <- distal_to(n, node.pointno = cut_nodes[[4]])
neuron.distal.points <- n$d[c(dist3, dist4),]

clear3d()
nview3d("frontal")
plot3d(n, col = "gray23", soma = T)
points3d(neuron.distal.points[,c('X','Y','Z')], col = "deepskyblue")
rglwidget()

Connectivity matrix of split neuron

#For our example lets just use the first cut point, and generate a connectivity matrix for the distal neuron
neuron.distal.points <- n$d[dist,]
#Generate connectivity matrix for neuron we are interested in
con_mat <- catmaid_get_connectors_between(post_skids = skid)
#subset connectivity matrix to only contain points in neuron.distal.points
neuron.distal.points #neuron.distal.points contains the following:

con_mat #and the connectivity matrix is a dataframe containing this informtation

#depending on whether you specified pre or post_skids in the catmaid_get_connectors_between we can subset con_mat by post_node_id or pre_node_id. In this example we are using post_skids and post_node_id to look at inputs to our neuron.
#subset con_mat by nodeid/PointNo from neuron.distal.points
con_mat$post_node_id %in% neuron.distal.points$PointNo  #returns a logical matrix, TRUE values are connections in our distal branch.

  [1]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
 [19] FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
 [37] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
 [55] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
 [73] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
 [91] FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[109] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[127] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[145] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[163] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[181] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[199] FALSE FALSE FALSE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE
[217] FALSE  TRUE  TRUE  TRUE  TRUE FALSE  TRUE  TRUE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE FALSE

con_mat[con_mat$post_node_id %in% neuron.distal.points$PointNo,] #don't forget the "," as con_mat is a dataframe.

dist_con_mat <- con_mat[con_mat$post_node_id %in% neuron.distal.points$PointNo,]

#We can even plot all of these connections to double check the connections are in the right place.
clear3d()
plot3d(n, soma = T, col = "grey")
points3d(neuron.distal.points[,c('X','Y','Z')], col = "deepskyblue")
points3d(dist_con_mat[,c("connector_x", "connector_y", "connector_z")], col = "green", size = 5)
rglwidget()

So you want to prune a neuron object instead?!

#Great! There's a handy function subset() in the nat package.
dist.n <- subset(n, dist)
names(dist.n)

[1] "NumPoints"    "StartPoint"   "BranchPoints" "EndPoints"    "nTrees"       "NumSegs"      "SegList"     
[8] "d"           

names(n)

 [1] "NumPoints"    "StartPoint"   "BranchPoints" "EndPoints"    "nTrees"       "NumSegs"      "SegList"     
 [8] "d"            "skid"         "connectors"   "tags"         NA             NA             "url"         
[15] "headers"     

#note how there is less information in the dist.n neuron object. It loosess connectivity information.

Task/Challenge: Try generate a connectivity matrix for the other cut points

2. Use the following code to generate a sampling spreadsheet.

#gives random ordering of pre-synaptic profiles
node = 29496430
skid = 6065216
#function to return the incoming pre-synaptic nodes for the selected skid proximal of the provided cut point
incoming_connections <- function(skid, node) {
  neuron <- read.neuron.catmaid(skid)
  dist <- distal_to(neuron, node.pointno = node)
  neuron.distal.points <- neuron$d[-dist,]
  
  
  
  #debug- graph to check selected distal points are in correct region
  nopen3d()
  plot3d(neuron, col = "gray23")
  points3d(neuron.distal.points[,c('X','Y','Z')], col = "deepskyblue")
  
  all_connectors <- catmaid_get_connectors_between(post_skids = skid)
  connectors = all_connectors[all_connectors$post_node_id %in% neuron.distal.points$PointNo,]
  #debug - graph to check all selected connectors in correct region
  nopen3d()
  plot3d(neuron, col = "black")
  points3d(connectors[,c('connector_x','connector_y','connector_z')])
  
  return(connectors)
}
#get incoming synapses 
n_incoming <- incoming_connections(skid = skid, node = node)
#randomise outgoing connectors for tracing downstream partners
perm = n_incoming[sample(nrow(n_incoming)),]
#debug - 
nopen3d()

glX 
 14 

neuron <- read.neuron.catmaid(skid)
plot3d(neuron)
points3d(n_incoming[,c('post_node_x','post_node_y','post_node_z')], col='blue')
points3d(perm[,c('post_node_x','post_node_y','post_node_z')], col = 'green', size = 8)
#—URL generator—
connector_URL <- function(dfrow){
  base = "https://neuropil.janelia.org/tracing/fafb/v14/"
  catmaid_url = paste0(base, "?pid=1")
  catmaid_url = paste0(catmaid_url, "&zp=", dfrow[,"pre_node_z"])
  catmaid_url = paste0(catmaid_url, "&yp=", dfrow[,"pre_node_y"])
  catmaid_url = paste0(catmaid_url, "&xp=", dfrow[,"pre_node_x"])
  catmaid_url = paste0(catmaid_url, "&tool=tracingtool")
  catmaid_url = paste0(catmaid_url, "&active_skeleton_id=", dfrow[,'pre_skid'])
  catmaid_url = paste0(catmaid_url, "&active_node_id=", dfrow[,"pre_node_id"])
  catmaid_url = paste0(catmaid_url, "&sid0=5&s0=0")
  
  invisible(catmaid_url)
}
#———
#generate URLs for each row
perm$URL = character(nrow(perm))
perm[,"URL"] = sapply(1:nrow(perm), function(x) perm[x, "URL"] = connector_URL(perm[x,]))
#write out as CSV to save in Google docs
write.csv(perm, file = 'test.csv')
library(googlesheets)
gs_ls()

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